Leveraging AI in the Agriculture Industry

The Importance of AI in Agriculture

Agriculture contributes to economic prosperity in both developed and developing nations, and AI technologies have given farmers a tool with which to produce greater output, at better quality, with fewer resources. There are already an estimated 75 million connected devices on farms today, and by 2050, it is believed that the average farm will generate more than 4 million data points every day, according to Science Direct. AI works by processing data input (everything from weather to soil to insects) and processing it incredibly fast so that farmers can make faster and better farming decisions. And it is able to learn as it goes and applies lessons learned to future problems. 

Four major factors are driving the growth of AI in agriculture: 

1. Growing population worldwide is creating greater demand for food and crop production. 
2. Farms are already adopting a range of information management systems and technologies to improve farm operations.
3. There is a great need to improve crop productivity, and AI can do it quickly and efficiently.
4. Governments around the world are supporting the adoption of modern agricultural techniques, including AI. 

Controlling Weeds With Agricultural Robotics

Weed control is a growing priority for farmers, as they face the dangers of herbicide resistance in many species of weeds. In a research study by the Weed Science Society of America, farmers are estimated to see annual losses of $43 billion because of uncontrolled weeds in corn and soybean crops alone. 

Today’s AI-driven equipment that can reduce 80-90 percent of herbicide use with vision processing and machine learning technology called “See and Spray.” It works by installing cameras that use computer vision and machine learning to make instant decisions on whether a plant is a weed or not. It processes images at 20 times per second as the equipment travels through a field, comparing it to a library of one million images. If it detects a weed, it determines the appropriate treatment for each plant and sprays only on the weeds. It knows the different sizes and species and is able to reduce human error and conduct weed control in record time. 

Next, in our learning of AI in agriculture, let us look at the predictive weather applications. 

AI in Predictive Weather Applications

Monitoring weather is obviously a key consideration for farmers everywhere, and AI is now being used to help farmers reduce the damage done by bad weather. The most important weather data that can be analyzed with AI include: 

Rainfall

AI can quickly analyze historical rainfall patterns over a certain period of time and make future predictions based on analytical algorithms. 

Temperature

AI algorithms help add analytics to the tracking of changing temperatures on a given day, month, or year, and provide a better outlook for future planning. 

Wind and Air Pressure

Wind direction and speed, as well as air pressure, are key predictors for storms and other adverse weather changes. 

Humidity

A key metric for preparing for how much rain can be expected and utilizing water supplies more intelligently. 

Data points from these categories can be gathered and consolidated in a single platform to monitor changes and develop a more predictive strategy to farming. They are even able to share data with other farmers to create forecasts for a larger community area. As much as 90 percent of crop losses are due to weather events, and one quarter of them could potentially be prevented by using predictive weather modeling. 

Next in our learning about AI in agriculture, let us find out how it helps with pest control.

Intelligently Fighting Pests

Today, AI-powered drones are helping farmers to fight disease and pests. A special drone camera used in Leones, Argentina, flies low over 150 acres of wheat and is able to check the crops, stalk by stalk, to see the signs of fungal infection that may threaten their production. The robot is powered by computer vision, a type of AI that is being increasingly used in these kinds of monitoring cameras. The AI engine is able to teach itself how to tag each potential threat, whether it’s an insect, fungus, or other danger. For farmers, the drones are the “eyes” of the operation, and AI and machine learning intelligence is the “brains.” 

Next, let us learn how making use of AI in agriculture helps the crops.

Improving Crop and Soil Health Monitoring

Soil is critical to healthy crops, and AI is becoming a key tool to monitor farmland soil. One Berlin-based company has developed a deep learning app that can identify potential defects and nutrient deficiencies in the soil. Software algorithms correlate foliage patterns with soil defects or the impact of insects or disease on crops. AI-driven image recognition sees the potential defects, reports it, and the farmer can be advised on different soil restoration solutions. 

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The Growing Importance of AI Upskilling

AI is providing a real lifeline for farmers and helping to keep the global agriculture industry strong and healthy. Both AI and ML Course and deep learning courses can give you the foundation for building a technology career that will have a great impact on industries like agriculture.